Precipitation Phase Prediction in Norway Using Neural Networks

We introduce a neural network for predicting precipitation phases across Norway. This method is trained on observation data from 52 airports throughout the country, with data from 3 winter seasons.

 

One of our main concerns is to have a method that works well in operation on Yr.no. It needs to be fast, and give results that are intuitive, so that it is easy to judge whether the method gives reasonable predictions.

 

The neural network uses predictors from the MetCoOp Ensemble Prediction System (MEPS) that forecasts weather across Norway. We use three predictor variables: air temperature at 1500 meters above the ground, air temperature at 200 meters above the ground, and the wet bulb temperature at 2m above the ground. The model output is the probability of snow for any given point in time and space, given that there is precipitation present.

 

The problem itself is a classification problem, so we have used a TensorFlow model with a sigmoid activation function in the output layer, together with a binary cross entropy loss function. Other than that, we have tested different configurations of hidden layers. It turns out that a simple configuration performs well and is robust with respect to overfitting.

 

 

Until now, MET Norway has used a simple threshold based method in operation, which is based on the wet bulb temperature at 2m height above the ground. This neural network approach offers a simple and data driven alternative to this, and improves the forecast in difficult weather scenarios.

 

To evaluate the method, we apply metrics such as accuracy and brier score, to compare our results with former methods.